Melanie Zeilinger

I am a research scientist in a joint program with the MPI for Intelligent Systems and the University of California Berkeley in the framework of a Marie Curie fellowship.

My research develops safe learning-based control techniques that enable automatic controllers to leverage online data, while guaranteeing satisfaction of safety conditions, e.g. in the form of constraint satisfaction, at all times. We address this problem by deriving both new theoretical frameworks, as well as highly efficient computational tools. My other research interests include distributed control and optimization for large-scale system networks.

The Marie Curie project COGENT applies these ideas to develop new modeling and control techniques for energy-efficient technologies. We specifically focus on the potential and challenges imposed by the integration of plug-in hybrid electric vehicles, whose primary use as a means of transportation makes the availability to the grid highly variable and dependent on driving behavior.

Uncertainties have long been recognized as a key difficulty for control, deteriorating performance or even putting system safety at risk. This issue has been classically addressed by robust controller design, making use of a deterministic bound on the uncertainty and designing the controller for all possible uncertainty realizations...

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems